Staff Data Scientist, Finance

Overview

Staff Data Scientist for Finance, leading Snowflake’s driver-based revenue modeling program to build reliable, explainable, production-grade decision systems.

What you'll do

• Own and scale a standardized driver-based revenue modeling framework across product categories.
• Define driver trees, attribution rules, measurement standards, assumptions, and taxonomies.
• Develop statistical, econometric, and machine learning methods to identify leading indicators and causal relationships.
• Forecast key drivers and revenue across short- and long-range horizons using multiple modeling approaches.
• Build self-service scenario, decomposition, and what-if tools with monthly and multi-year views.
• Set standards for evaluation, backtesting, stability testing, reconciliation, confidence intervals, and change documentation.
• Productionize and operate frequently refreshed pipelines and applications with quality gates and monitoring.

What you'll need

• 5+ years building and operating production-grade statistical, forecasting, econometric, or ML systems with business impact.
• Advanced degree in a quantitative field (or equivalent practical experience).
• Strong hands-on experience with business-critical forecasting or driver/unit-economics modeling.
• Deep modeling skills including time-series forecasting and causal inference, plus panel/cohort and probabilistic methods.
• Ability to work with imperfect telemetry and define defensible assumptions while identifying data gaps.
• Strong proficiency in Python and SQL for large datasets and productionization.
• Experience with large-scale data systems and modern data platforms such as Snowflake, BigQuery, Redshift, or Spark.

Details

• Role involves partnering with Product Finance, Product Data Science, Finance Data and Analytics, Analytics Engineering, Product, and go-to-market teams.